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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are designing a generative AI model to generate customer support responses. During testing, you notice that the model frequently outputs gendered language when referring to certain professions, reinforcing stereotypes.
Which of the following strategies would most effectively reduce bias in the model' responses?

A) Reduce the maximum token limit so that the model generates shorter responses, minimizing the chance for bias.
B) Increase the diversity of the dataset used to train the model, ensuring that all professions are equally represented.
C) Apply a post-processing filter that removes any gendered language after the model generates the response.
D) Train the model with a lower learning rate to make it less sensitive to biased patterns in the data.

2. You are fine-tuning a general-purpose language model on a medical dataset to generate summaries of patient consultations. After fine-tuning, you notice that the model sometimes generates hallucinations-statements that are factually incorrect or irrelevant to the specific domain. You suspect that the fine-tuning process did not sufficiently align the model with the medical domain.
Which of the following is the most effective technique to reduce hallucinations during fine-tuning?

A) Increase the model's batch size during training
B) Use domain-specific tokenization during fine-tuning
C) Add more general-purpose data to the fine-tuning dataset
D) Increase the number of layers in the model

3. When planning data elements for optimizing a generative AI model's performance in IBM watsonx, which of the following strategies should be prioritized to ensure data quality and model accuracy?

A) Focus on using only labeled data, as generative AI models do not perform well on unlabeled data.
B) Ensure all data used is unstructured to allow for maximum model flexibility.
C) Prioritize balanced datasets to minimize model bias and enhance generalization.
D) Leverage data augmentation techniques only on the smallest available dataset.

4. When using few-shot prompting, which of the following is a common limitation you may encounter while working with Watsonx AI for text generation tasks?

A) Few-shot prompting requires the model to be retrained with additional examples.
B) The model may overfit to the few examples provided, leading to overly specific responses.
C) Few-shot prompting is only effective for classification tasks and cannot be used for text generation.
D) Few-shot prompting cannot handle open-ended tasks, as it only works for structured data inputs.

5. You are training a generative AI model using IBM's Tuning Studio and want to optimize its performance. You aim to avoid both overfitting and underfitting by carefully selecting the appropriate number of epochs.
Which of the following strategies would best help you set the optimal number of epochs during the tuning process?

A) Use a single epoch to avoid overfitting
B) Gradually increase the number of epochs until the loss on the training set reaches zero
C) Set the number of epochs equal to the size of the dataset
D) Set a high number of epochs and use early stopping to determine the optimal point

Solutions: